English
At times people get bogged down in measuring the operational hours per day or per week or per month or even over an entire project or year(s). Others prefer to look at the efficiency, how much are we doing how fast? Control charts are then sometimes constructed to follow these on constituents thereof up and see what might be amiss if anything. And though there is of course merit in doing so it does remain a rather disjointed approach. One tends to end up with a lot of Key Performance Indicators (KPIs) making it at times a challenge to still see the wood for the trees. So what might offer you a better “one number tells it all approach”?
Have a go at trying to establish the Overall Equipment Effectiveness (OEE). Note that we talk here about effectiveness and not efficiency. There is a marked difference and the best way to describe it I find is given by “efficiency” tends to be all about how fast you do stuff (often at the lowest possible cost) whilst “effectiveness” is more about how fast does what you are doing make a difference for the good for your project. One might say that the value of what you are doing is given more weight there.
OEE can be represented mathematically by means of the following equation:
OEE = (Availability)*(Performance)*(Quality)
In this the three components making up the OEE can be detailed as follows:
- Availability: This represents the percentage of scheduled time that the operation is available to operate, one might say the Operational Hours (OH) mentioned above. Often people also refer to this simply as uptime.
- Performance: This represents the speed at which the plant, piece of kit, or whatever runs as a percentage of its designed speed.
- Quality: This is the component that is the most often overlooked one and represents the actually good pieces produced as a percentage of the total units that have been started on. In some literature this is referred to as the First Pass Yield (FPY), e.g. the resulting pieces that require no rework.
It can be seen that essentially OEE measures effectiveness based on scheduled hours. And it does so unforgivingly as most trying to use this parameter conscientiously have already found out.
If we go though it with the aid of a worked example one might fond something like this:
Your production unit is scheduled to run for a 12-hour (720 minute) shift with a 60-minute scheduled break for repairs and lunch of some of the crew and you experience a total of 124 minutes of unplanned (breakdown) downtime. This would put your Availability at:
Availability = (720 – 60 – 124)/720 = 74.44%
The above tells us that the operating time is 720 – 60 – 124 = 536min
Let’s say for the sake of his example the standard production rate of units being spat out is 2widgets/min, this being just widgets not necessarily “good” widgets. Then that means a pace of 0.5min/widget. If that day the actual numbers of widgets made was 916 then that would place your Productivity at:
Productivity = (0.5*916)/536 = 85.45%
If we now on closer inspection find that 59 were defective in some manner or other that would put your Quality at:
Quality = (916-59)/916 = 93.56%
Placing your OEE at 74.44%*85.45%*93.56% = 59.51%
“Only 59%?” some might ask. Well, I did mention “unforgivingly” earlier on, didn’t I? If the 3 components making up the OEE are all 90% your EOO is already down to 73%!!!
Erroneously OEE is sometimes seen as the sole domain of production or quality engineers on the work floor. By simply introducing the concept of Loading this can be made directly applicable for scheduling professionals, accountants etc. The leading metric you then have is often termed the Total Effective Equipment Performance.
TEEP = Loading * OEE
- Loading here represents the percentage of total calendar time that is actually scheduled for operation and is as such a pure measurement of schedule effectiveness excluding the effects of how well that operation itself may perform
As a simple example, we could look at a factory for the above widgets in the country Utopia where you have 5 working days of 24 hours per week, 4 obligatory weeks of national vacation for all and 10 public holidays all on weekdays. In that case your loading would become:
Loading = ((5d * 24hrs)*(52 – 4) – 10d*24hrs)/(365d*24hrs) = 63.01%
Making your TEEP here 37.50%
The numerator in the above is often termed the Planned Production Time (PPT) whilst the devisor is the Calendar Time (CT) or All Time (AT). In all these it is important to keep an eye on the time interval or sampling frequency you are looking at. Simply multiplying the yearly Loading with a daily OEE is unlikely to yield very practically actionable result for you.
But having said that, are OEE and TEEP truly useful KPIs that can kindle focus and performance? You bet!!! In that sense they are like a charity’s fundraising barometer, the bells or light rail next to the strong man challenge at the fair or the concentric circles on the shooting target… it will draw your attention in like nothing else. So have a go; and maybe next time we’ll have a look at True Downtime Cost (TDC)!
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